1. Field
The present disclosure relates generally to associative memory management and in particular to finding the presence of ambiguities in data stored within an associative memory for the purpose of reducing information obfuscation which can improve decision making.
2. Background
When analyzing data, ambiguous data can cause confusion, delay, and possibly errors in an analysis. As used herein, “ambiguous data” may be a set of data that is associated with two or more distinct categories. The “set of data” may be a value, which may be a number, an alphanumeric string of characters, a symbol, or as described elsewhere herein. “Categories” are groupings of data as arranged by a user or a data processing system.
For instance, the number “123-456-7890” could be a phone number, or perhaps could be a part number, or perhaps could be associated with some other category. In this case, the number “123-456-7890” is a “set of data” or a value. This set of data is associated with both a phone number and a part number. The phone number is a first category and the part number is a second category. In some cases a user could not know, by viewing the number alone, to which category the number belonged. Or, in a broader sense, the user might not be able to distinguish if the data might have been ambiguous in the first place, regardless of the presence of a second category. Thus, a comparison may not be needed in order to find the presence of ambiguous data.
A multiplicity of ambiguities may arise where the same number sequence is associated with many different categories, or perhaps special characters such as the two hyphens in 123-456-7890 are ignored by a search engine, thereby creating even greater numbers of ambiguities. Furthermore, a user may view the set of data as belonging to multiple categories, thereby increasing the complexity of the data analysis. However, a user may not even be aware of the presence of ambiguous data in a data set, which may be perhaps more problematic.
As indicated above, in some instances, if these ambiguities are not identified, errors in a data analysis may result. For example, ambiguous data may provide misleading statistics, or perhaps inaccurate counts when totaling large amounts of data. In addition, when searching large data sets, ambiguous data can cloud result sets and cause frustration when a user is trying to obtain useful information. For example, if a user enters the part number into a search engine, the user may see in the returned results house numbers, phone numbers, and many other categories which satisfy the number's form, but are of no interest to the user. Therefore, it may be advantageous for a user to understand if a possibility of ambiguity exists in one or more data sources in order to avoid issues with data obfuscation, and thereby improve decision making.